{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2.5 Jupyter Notebook 的使用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code模式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello World!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Markdown模式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hello World!\n",
    "## Hello World!\n",
    "### Hello World!\n",
    "Hello World!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LaTex语法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$f(x) = 3x+7$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.1 Python 的基本语法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 打印输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello,World\n",
      "----------\n",
      "Hello\n",
      "World\n",
      "----------\n",
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello,World\")\n",
    "print(\"-\"*10)\n",
    "print(\"Hello\")\n",
    "print(\"World\")\n",
    "print(\"-\"*10)\n",
    "print(\"Hello\",\"World\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 代码中的注释"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello,World\n"
     ]
    }
   ],
   "source": [
    "#一行注释\n",
    "print(\"Hello,World\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello,World\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "多行注释\n",
    "多行注释\n",
    "多行注释\n",
    "\"\"\"\n",
    "'''\n",
    "多行注释\n",
    "多行注释\n",
    "多行注释\n",
    "'''\n",
    "print(\"Hello,World\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 代码的缩进"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (<ipython-input-5-2f7781d1dc42>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-5-2f7781d1dc42>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    print(\"Hello,World\")\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello,World\")\n",
    " print(\"Hello,World\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "----------\n",
      "1\n",
      "----------\n",
      "2\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    print(i)\n",
    "    print(\"-\"*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多行语句的分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello,World.Hello,World.Hello,World.Hello,World.\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello,World.Hello,World.\\\n",
    "Hello,World.Hello,World.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.2 Python变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "10.0\n",
      "Hello,World\n"
     ]
    }
   ],
   "source": [
    "int_num = 10\n",
    "float_num = 10.00\n",
    "string = \"Hello,World\"\n",
    "\n",
    "print(int_num)\n",
    "print(float_num)\n",
    "print(string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello,World\n",
      "Hello,World\n",
      "Hello,World\n"
     ]
    }
   ],
   "source": [
    "string1 = string2 = string3 = \"Hello,World\"\n",
    "\n",
    "print(string1)\n",
    "print(string2)\n",
    "print(string3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n",
      "World\n",
      "Hello,World\n"
     ]
    }
   ],
   "source": [
    "string1, string2, string3 = \"Hello\", \"World\",\"Hello,World\"\n",
    "\n",
    "print(string1)\n",
    "print(string2)\n",
    "print(string3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n",
      "Hello,World\n",
      "World\n"
     ]
    }
   ],
   "source": [
    "string1, string2, string3 = \"Hello\",\"Hello,World\",\"World\"\n",
    "\n",
    "print(string1)\n",
    "print(string2)\n",
    "print(string3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.3 常用的数据类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "10.0\n"
     ]
    }
   ],
   "source": [
    "int_num = 10\n",
    "float_num = 10.00\n",
    "\n",
    "print(int_num)\n",
    "print(float_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n",
      "10.000000\n",
      "10.00\n",
      "10.0000\n"
     ]
    }
   ],
   "source": [
    "float_num = 10.000\n",
    "\n",
    "print(float_num)\n",
    "print(\"%f\" % float_num)\n",
    "print(\"%.2f\" % float_num)\n",
    "print(\"%.4f\" % float_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello,World_@1\n",
      "Hello,World_@2\n"
     ]
    }
   ],
   "source": [
    "string1 = 'Hello,World_@1'\n",
    "string2 = \"Hello,World_@2\"\n",
    "\n",
    "print(string1)\n",
    "print(string2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H\n",
      "Hello,World\n",
      "Hello\n",
      "d\n",
      "World\n",
      "World\n",
      "Hello\n"
     ]
    }
   ],
   "source": [
    "string = 'Hello,World'\n",
    "string0 = string[0]\n",
    "string1 = string[0:13]\n",
    "string2 = string[0:5]\n",
    "string3 = string[-1]\n",
    "string4 = string[-5:]\n",
    "string5 = string[6:]\n",
    "string6 = string[:5]\n",
    "\n",
    "print(string0)\n",
    "print(string1)\n",
    "print(string2)\n",
    "print(string3)\n",
    "print(string4)\n",
    "print(string5)\n",
    "print(string6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hello,World', 100, 10.0]\n",
      "Hello,World\n",
      "[100, 10.0]\n",
      "10.0\n",
      "['Hello,World', 100, 10.0, 123, 'Hi']\n"
     ]
    }
   ],
   "source": [
    "list1 = [ \"Hello,World\", 100 , 10.00 ]\n",
    "list2 = [123, 'Hi']\n",
    "\n",
    "print(list1) # 输出整个list1 列表元素\n",
    "print(list1[0]) # 输出列表的第1 个元素\n",
    "print(list1[1:]) # 输出从第1 个索引开始至列表末尾的所有元素\n",
    "print(list1[-1]) # 输出列表的最后一个元素\n",
    "print(list1 + list2) # 输出列表的组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hello,World', 100, 10.0]\n",
      "['Hello,World,Hi', 100, 10.0]\n"
     ]
    }
   ],
   "source": [
    "list_old = [ \"Hello,World\", 100 , 10.00 ]\n",
    "print(list_old) # 输出整个list_old 列表元素\n",
    "\n",
    "list_old[0] = \"Hello,World,Hi\" # 对list_old 的第1 个元素重新赋值\n",
    "list_new = list_old\n",
    "print(list_new) # 输出整个list_old 列表元素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 元组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Hello,World', 100, 10.0)\n",
      "Hello,World\n",
      "(100, 10.0)\n",
      "10.0\n",
      "('Hello,World', 100, 10.0, 123, 'Hi')\n"
     ]
    }
   ],
   "source": [
    "tuple1 = (\"Hello,World\", 100 , 10.00 )\n",
    "tuple2 = (123, 'Hi')\n",
    "\n",
    "print(tuple1) # 输出整个tuple1 元组元素\n",
    "print(tuple1[0]) # 输出元组的第1 个元素\n",
    "print(tuple1[1:]) # 输出从第1 个索引开始至列表末尾的所有元素\n",
    "print(tuple1[-1]) # 输出元组的最后一个元素\n",
    "print(tuple1 + tuple2) # 输出元组的组合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is one\n",
      "This is two\n",
      "{'name': 'Tang', 'num': 7272, 'city': 'GL'}\n",
      "dict_keys(['name', 'num', 'city'])\n",
      "dict_values(['Tang', 7272, 'GL'])\n"
     ]
    }
   ],
   "source": [
    "dict_1 = {}\n",
    "dict_1[\"one\"] = \"This is one\"\n",
    "dict_1[2] = \"This is two\"\n",
    "dict_info = {\"name\": \"Tang\", \"num\":7272, \"city\": \"GL\"}\n",
    "\n",
    "print (dict_1[\"one\"]) # 输出键值为one 的值\n",
    "print (dict_1[2]) # 输出键值为 2 的值\n",
    "print (dict_info) # 输出整个dict_info 字典\n",
    "print (dict_info.keys()) # 输出dict_info 的所有键值\n",
    "print (dict_info.values()) # 输出dict_info 的所有值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.4 Python运算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 算术运算符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a + b = 7\n",
      "a - b = 3\n",
      "a / b = 2.5\n",
      "a * b = 10\n",
      "a // b = 2\n",
      "a % b = 1\n",
      "a ** b = 25\n"
     ]
    }
   ],
   "source": [
    "a = 5 #定义变量a\n",
    "b = 2 #定义变量b\n",
    "\n",
    "c = a + b #进行加法运算\n",
    "print(\"a + b =\", c)\n",
    "\n",
    "c = a - b #进行减法运算\n",
    "print(\"a - b =\", c)\n",
    "\n",
    "c = a / b #进行除法运算\n",
    "print(\"a / b =\", c)\n",
    "\n",
    "c = a * b #进行乘法运算\n",
    "print(\"a * b =\", c)\n",
    "\n",
    "c = a // b #进行取整运算\n",
    "print(\"a // b =\", c)\n",
    "\n",
    "c = a % b #进行取模运算\n",
    "print(\"a % b =\", c)\n",
    "\n",
    "c = a ** b #进行幂运算\n",
    "print(\"a ** b =\", c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a + b = 7\n",
      "a - b = 3\n",
      "a / b = 2.5\n",
      "a * b = 10\n",
      "a // b = 2\n",
      "a % b = 1\n",
      "a ** b = 25\n"
     ]
    }
   ],
   "source": [
    "a = 5 #定义变量a\n",
    "b = 2 #定义变量b\n",
    "\n",
    "a += b #进行加法运算\n",
    "print(\"a + b =\", a)\n",
    "\n",
    "a = 5 #对变量进行重置\n",
    "a -= b #进行减法运算\n",
    "print(\"a - b =\", a)\n",
    "\n",
    "a = 5 #对变量进行重置\n",
    "a /= b #进行除法运算\n",
    "print(\"a / b =\", a)\n",
    "\n",
    "a = 5 #对变量进行重置\n",
    "a *= b #进行乘法运算\n",
    "print(\"a * b =\", a)\n",
    "\n",
    "a = 5 #对变量进行重置\n",
    "a //= b #进行取整运算\n",
    "print(\"a // b =\", a)\n",
    "\n",
    "a = 5 #对变量进行重置\n",
    "a %= b #进行取模运算\n",
    "print(\"a % b =\", a)\n",
    "\n",
    "a = 5 #对变量进行重置\n",
    "a **= b #进行幂运算\n",
    "print(\"a ** b =\", a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 比较运算符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "True\n",
      "True\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "a = 5 #定义变量a\n",
    "b = 2 #定义变量b\n",
    "\n",
    "print(a == b) #判断a 是否等于b\n",
    "print(a != b) #判断a 是否不等于b\n",
    "print(a > b) #判断a 是否大于b\n",
    "print(a >= b) #判断a 是否大于等于b\n",
    "print(a < b) #判断a 是否小于b\n",
    "print(a <= b) #判断a 是否小于等于b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "a = 5 #定义变量a\n",
    "b = 2 #定义变量b\n",
    "c = 4 #定义变量c\n",
    "\n",
    "print(a == b == c) #判断a 是否等于b 且b 等于c\n",
    "print(a > b > c) #判断a 是否大于b 且b 大于c\n",
    "print(a < b <c) #判断a 是否小于b 且b 小于c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 布尔运算符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "a = True #定义变量a\n",
    "b = False #定义变量b\n",
    "\n",
    "print(a and b) #与预算\n",
    "print(a or b) #或运算\n",
    "print(not a) #非运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "True\n",
      "False\n",
      "False\n",
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "a = 2 #定义变量a\n",
    "b = 1 #定义变量b\n",
    "c = 3 #定义变量c\n",
    "\n",
    "print(a>b and a<b) #与预算\n",
    "print(a>b or a<b) #或运算\n",
    "print(not a<b) #非运算\n",
    "print(not a>b) #非运算\n",
    "print(a>b and c<b and c>a)\n",
    "print(a>b or a<b and c>a)\n",
    "print(a>b or a<b or c>a)\n",
    "print(a>b and a<b or c>a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 成员运算符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "list_1 = [\"I\",\"am\",\"super\",\"man\"]\n",
    "a = \"super\"\n",
    "b = 1\n",
    "\n",
    "print(a in list_1)\n",
    "print(b in list_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 身份运算符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a 的内存地址： 1515992371120\n",
      "b 的内存地址： 1515992370096\n",
      "a is b False\n",
      "a is not b True\n",
      "a == b True\n",
      "a 的内存地址： 1790504336\n",
      "b 的内存地址： 1790504336\n",
      "a is b True\n",
      "a is not b False\n",
      "a == b True\n"
     ]
    }
   ],
   "source": [
    "a = 500 #定义变量a\n",
    "b = 500 #定义变量b\n",
    "print(\"a 的内存地址：\",id(a))\n",
    "print(\"b 的内存地址：\",id(b))\n",
    "print(\"a is b\",a is b)\n",
    "print(\"a is not b\",a is not b)\n",
    "print(\"a == b\",a == b)\n",
    "\n",
    "a = 10 #定义变量a\n",
    "b = 10 #定义变量b\n",
    "print(\"a 的内存地址：\",id(a))\n",
    "print(\"b 的内存地址：\",id(b))\n",
    "print(\"a is b\",a is b)\n",
    "print(\"a is not b\",a is not b)\n",
    "print(\"a == b\",a == b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.5 Python条件判断语句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is equal 10\n"
     ]
    }
   ],
   "source": [
    "number = 10\n",
    "if number == 10: # 条件判断语句\n",
    "    print(\"The number is equal\", number) # 输出符合条件判断语句的代码块\n",
    "else: # 条件判断语句\n",
    "    print(\"The number is not equal 10.\") # 输出符合条件判断语句的代码块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is greater than 10.\n"
     ]
    }
   ],
   "source": [
    "number = 15\n",
    "if number == 10: # 条件判断语句\n",
    "    print(\"The number is equal\", number) # 输出符合条件判断语句的代码块\n",
    "elif number > 10: # 条件判断语句\n",
    "    print(\"The number is greater than 10.\") # 输出符合条件判断语句的代码块\n",
    "elif number < 10: # 条件判断语句\n",
    "    print(\"The number is less than 10.\") # 输出符合条件判断语句的代码块\n",
    "else: # 条件判断语句\n",
    "    print(\"Error.\") # 输出符合条件判断的代码块"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.6 Python循环语句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is 0\n",
      "The number is 1\n",
      "The number is 2\n",
      "The number is 3\n",
      "The number is 4\n",
      "The number is 5\n",
      "The number is 6\n",
      "The number is 7\n",
      "The number is 8\n",
      "The number is 9\n"
     ]
    }
   ],
   "source": [
    "number = 0\n",
    "while (number < 10):\n",
    "    print( \"The number is\", number)\n",
    "    number += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is 0\n",
      "The number is 1\n",
      "The number is 2\n",
      "The number is 3\n",
      "The number is 4\n",
      "The number is 5\n",
      "The number is 6\n",
      "The number is 7\n",
      "The number is 8\n",
      "The number is 9\n"
     ]
    }
   ],
   "source": [
    "number = 10\n",
    "for i in range(10):\n",
    "    if i<number:\n",
    "        print( \"The number is\", i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is 0\n",
      "The number is 1\n",
      "The number is 2\n",
      "The number is 3\n",
      "The number is 4\n"
     ]
    }
   ],
   "source": [
    "number = 10\n",
    "for i in range(10):\n",
    "    if i == 5:\n",
    "        break\n",
    "    if i<number:\n",
    "        print( \"The number is\", i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is 0\n",
      "The number is 1\n",
      "The number is 2\n",
      "The number is 3\n",
      "The number is 4\n",
      "The number is 6\n",
      "The number is 7\n",
      "The number is 8\n",
      "The number is 9\n"
     ]
    }
   ],
   "source": [
    "number = 10\n",
    "for i in range(10):\n",
    "    if i == 5:\n",
    "        continue\n",
    "    if i<number:\n",
    "        print( \"The number is\", i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number is 0\n",
      "The number is 1\n",
      "The number is 2\n",
      "The number is 3\n",
      "The number is 4\n",
      "The number is 5\n",
      "The number is 6\n",
      "The number is 7\n",
      "The number is 8\n",
      "The number is 9\n"
     ]
    }
   ],
   "source": [
    "number = 10\n",
    "for i in range(10):\n",
    "    if i == 5:\n",
    "        pass\n",
    "    if i<number:\n",
    "        print( \"The number is\", i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "a_list = [1,2,3,4,5,6]\n",
    "for i in a_list:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.7 Python中的函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 定义函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello,World.\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "def function():\n",
    "    print(\"Hello,World.\")\n",
    "    return\n",
    "\n",
    "a = function()\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What you say is: Hi\n",
      "What you say is: Hello,World.\n"
     ]
    }
   ],
   "source": [
    "def function(string = \"Hi\"):\n",
    "    print(\"What you say is:\", string)\n",
    "    return\n",
    "\n",
    "function()\n",
    "function(string = \"Hello,World.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 函数的参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What you say is: Hello,World.\n",
      "What you say is: Hi\n",
      "What you say is: Hello World\n",
      "10\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "def function1(string): #定义必备参数\n",
    "    print(\"What you say is:\", string)\n",
    "    return\n",
    "\n",
    "def function2(string = \"Hi\"): #定义默认参数\n",
    "    print(\"What you say is:\", string)\n",
    "    return\n",
    "\n",
    "def function3(string2 = \"World\",string1 = \"Hello\"): #定义关键字参数\n",
    "    print(\"What you say is:\", string1, string2)\n",
    "    return\n",
    "\n",
    "def function4(arg1, *arg2): #定义不定长参数\n",
    "    print(arg1)\n",
    "    for i in arg2:\n",
    "        print(i)\n",
    "    return\n",
    "\n",
    "function1(\"Hello,World.\")\n",
    "function2()\n",
    "function3()\n",
    "function4(10, 1, 2, 3, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.8 Python中的类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 类的创建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student name: Tang Student age: 20\n",
      "Student name: Wu Student age: 22\n",
      "Total Student: 2\n"
     ]
    }
   ],
   "source": [
    "class Student:\n",
    "    student_Count = 0\n",
    "    def __init__(self, name, age):\n",
    "        self.name = name\n",
    "        self.age = age\n",
    "        Student.student_Count += 1\n",
    "    \n",
    "    def dis_student(self):\n",
    "        print(\"Student name:\",self.name,\"Student age:\",self.age)\n",
    "        \n",
    "student1 = Student(\"Tang\", \"20\") #创建第1 个Student 对象\n",
    "student2 = Student(\"Wu\", \"22\") #创建第2 个Student 对象\n",
    "\n",
    "student1.dis_student()\n",
    "student2.dis_student()\n",
    "print(\"Total Student:\", Student.student_Count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 类的继承"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "school name is: Wu\n",
      "name is: Wu\n",
      "age is: 20\n",
      "age is: 22\n"
     ]
    }
   ],
   "source": [
    "class People:\n",
    "    def __init__(self, name, age):\n",
    "        self.name = name\n",
    "        self.age = age\n",
    "        \n",
    "    def dis_name(self):\n",
    "        print(\"name is:\",self.name)\n",
    "\n",
    "    def set_age(self, age):\n",
    "        self.age = age\n",
    "        \n",
    "    def dis_age(self):\n",
    "        print(\"age is:\",self.age)\n",
    "        \n",
    "class Student(People):\n",
    "    def __init__(self, name, age, school_name):\n",
    "        self.name = name\n",
    "        self.age = age\n",
    "        self.school_name = school_name\n",
    "\n",
    "    def dis_student(self):\n",
    "        print(\"school name is:\",self.name)\n",
    "        \n",
    "student = Student(\"Wu\", \"20\", \"GLDZ\") #创建一个Student 对象\n",
    "student.dis_student() #调用子类的方法\n",
    "student.dis_name() #调用父类的方法\n",
    "student.dis_age() #调用父类的方法\n",
    "student.set_age(22) #调用父类的方法\n",
    "student.dis_age() #调用父类的方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 类的重写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is Child.\n"
     ]
    }
   ],
   "source": [
    "class Parent: #定义父类\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def print_info(self):\n",
    "        print(\"This is Parent.\")\n",
    "        \n",
    "class Child(Parent): #定义子类\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def print_info(self): #对父类的方法进行重写\n",
    "        print(\"This is Child.\")\n",
    "        \n",
    "child = Child()\n",
    "child.print_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4.2多维数组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 创建多维数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.array([[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "only 2 non-keyword arguments accepted",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-50-4b56e0bc604a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: only 2 non-keyword arguments accepted"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.array(1,2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.ones([2,3]) #创建全1 的数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  2.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.ones([2,3]) #创建全1 的数组：\n",
    "a[1,2] = 2 #对数组中的元素进行覆盖\n",
    "\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.zeros([2,3]) #创建全0 的数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.],\n",
       "       [ 1.,  1.,  2.]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.empty([2,3]) #创建随机初始化的数组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 多维数组的常用属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.ones([2,3]) #创建全1 的数组\n",
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.ones([2,3]) #创建全1 的数组\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 3]\n",
      " [3 4]]\n",
      "(2, 2)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.matrix([[2,3],[3,4]]) #创建矩阵\n",
    "print(a)\n",
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.ones([2,3]) #创建全1 的数组\n",
    "print(a.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.ones([2,3]) #创建全1 的数组\n",
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.ones([2,3], dtype= np.int32) #创建全1 的数组\n",
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.ones([2,3], dtype= np.int32) #创建全1 的数组\n",
    "a.itemsize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数组的打印"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]]\n",
      "\n",
      " [[ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.ones([2,5,3]) #创建全1 的数组\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   0    1    2 ..., 1997 1998 1999]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.arange(2000) #创建有2000 个元素的数组\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4.3 多维数组的基本操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数组的算术运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a - b = [-3 -3 -3]\n",
      "a + b = [5 7 9]\n",
      "a / b = [ 0.25  0.4   0.5 ]\n",
      "a * b = [ 4 10 18]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([1,2,3])\n",
    "b = np.array([4,5,6])\n",
    "\n",
    "print(\"a - b =\",a-b) #打印a-b 的结果\n",
    "print(\"a + b =\",a+b) #打印a+b 的结果\n",
    "print(\"a / b =\",a/b) #打印a/b 的结果\n",
    "print(\"a * b =\",a*b) #打印a*b 的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a * b = [ 4 10 18]\n",
      "Matrix1: a * b = 32\n",
      "Matrix2: a * b = 32\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([1,2,3])\n",
    "b = np.array([4,5,6])\n",
    "print(\"a * b =\",a*b) #打印a*b 的结果\n",
    "\n",
    "c = a.dot(b)\n",
    "print(\"Matrix1: a * b =\",c) #打印a*b 的结果\n",
    "\n",
    "d = np.dot(a,b)\n",
    "print(\"Matrix2: a * b =\",c) #打印a*b 的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a * 2 = [2 4 6]\n",
      "a / 2 = [ 0.5  1.   1.5]\n",
      "a - 2 = [-1  0  1]\n",
      "a + 2 = [3 4 5]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([1,2,3])\n",
    "\n",
    "print(\"a * 2 =\",a*2)\n",
    "print(\"a / 2 =\",a/2)\n",
    "print(\"a - 2 =\",a-2)\n",
    "print(\"a + 2 =\",a+2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数组的自身运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min of array: 1\n",
      "max of array: 3\n",
      "sum of array: 6\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([1,2,3])\n",
    "\n",
    "print(\"min of array:\",a.min())\n",
    "print(\"max of array:\",a.max())\n",
    "print(\"sum of array:\",a.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min of array: 1\n",
      "min of array: [1 2 1]\n",
      "min of array: [1 1]\n",
      "max of array: 3\n",
      "max of array: [3 2 3]\n",
      "max of array: [3 3]\n",
      "sum of array: 12\n",
      "sum of array: [4 4 4]\n",
      "sum of array: [6 6]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([[1,2,3],\n",
    "[3,2,1]])\n",
    "\n",
    "print(\"min of array:\",a.min())\n",
    "print(\"min of array:\",a.min(axis=0))\n",
    "print(\"min of array:\",a.min(axis=1))\n",
    "print(\"max of array:\",a.max())\n",
    "print(\"max of array:\",a.max(axis=0))\n",
    "print(\"max of array:\",a.max(axis=1))\n",
    "print(\"sum of array:\",a.sum())\n",
    "print(\"sum of array:\",a.sum(axis=0))\n",
    "print(\"sum of array:\",a.sum(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exp of array: [[  2.71828183   7.3890561   20.08553692]]\n",
      "Sqrit of array: [[ 1.          1.41421356  1.73205081]]\n",
      "Square of array: [[1 4 9]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([[1,2,3]])\n",
    "\n",
    "print(\"Exp of array:\",np.exp(a))\n",
    "print(\"Sqrit of array:\",np.sqrt(a))\n",
    "print(\"Square of array:\",np.square(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 随机数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.37454012  0.95071431  0.73199394]\n",
      " [ 0.59865848  0.15601864  0.15599452]]\n",
      "[[ 1.57921282  0.76743473 -0.46947439]\n",
      " [ 0.54256004 -0.46341769 -0.46572975]]\n",
      "6\n",
      "6\n",
      "0.45543839870822056\n",
      "2.666236704694229\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(42)\n",
    "\n",
    "print(np.random.rand(2,3))\n",
    "print(np.random.randn(2,3))\n",
    "print(np.random.randint(1,10))\n",
    "print(np.random.binomial(6,1))\n",
    "print(np.random.beta(2,3))\n",
    "print(np.random.normal(2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 索引、切片和迭代"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n",
      "[0 1 2 3 4]\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.arange(10)\n",
    "print(a) #输出整个数组\n",
    "print(a[:5]) #输出数组的前五个元素\n",
    "\n",
    "for i in a: #迭代输出数组的全部元素\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "----------\n",
      "[4 5 6]\n",
      "----------\n",
      "[[2 3]\n",
      " [5 6]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([[1,2,3],\n",
    "        [4,5,6],\n",
    "        [7,8,9]])\n",
    "\n",
    "print(a) #输出整个数组\n",
    "print(\"-\"*10)\n",
    "print(a[1]) #输出指定维度的数据\n",
    "print(\"-\"*10)\n",
    "print(a[0:2, 1:3]) #输出指定维度的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "----------\n",
      "[4 5 6]\n",
      "----------\n",
      "[7 8 9]\n",
      "----------\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([[1,2,3],\n",
    "        [4,5,6],\n",
    "        [7,8,9]])\n",
    "\n",
    "for i in a:\n",
    "    print(i)\n",
    "    print(\"-\"*10)\n",
    "    \n",
    "for i in a:\n",
    "    for j in i:\n",
    "        print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in a.flat:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.5.2 创建图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 线型图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x160fa7c9208>]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KpHNniSPEasvaqZPs26ycSopxbACYuRrAHwG8D2AFgFeZ+SsiuoKIrgitNgPA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\n8v8BCFu7FZ0MriAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160f98d8f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(42)\n",
    "x = np.random.randn(30)\n",
    "plt.plot(x, \"r--o\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 线条颜色、标记形状和线型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x160fa89eba8>,\n",
       " <matplotlib.lines.Line2D at 0x160fa89ecf8>,\n",
       " <matplotlib.lines.Line2D at 0x160fa8a7cf8>,\n",
       " <matplotlib.lines.Line2D at 0x160fa8a7e48>]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6fzTa5G0oqC+w3mCuIOauQREBKSnqSXJennpffWs9Gtsb9bI66lK4qBCf3fGZVcf/b8e2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\n/w9MKZk3VWQYWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160fa820ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "a = np.random.randn(30)\n",
    "b = np.random.randn(30)\n",
    "c = np.random.randn(30)\n",
    "d = np.random.randn(30)\n",
    "plt.plot(a, \"r--o\", b, \"b-*\", c, \"g-.+\", d, \"m:x\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 标签和图例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x160fa94acc0>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ff4L27WmFXLr5sDuR248+Aq69Fjh8GKWlfleYx0OGjKhsN4WT4j81ubmUjuvE\niRyi/1VhIRU56TWoGzOGrlW/dneVSMU1SmoSMEbv26JFzjQ/gJoa8g8ZFU0MHgy8/ro5dbOJ8JSd\neGJ49h9ti2PXLmDt2ui7qYDoCkcGAOVHUOq9T81pjLGNjLGFjLEBejtjjE1ljK1ljK2tstK+HNB1\nmVQ9SVckKxYHY0BGehNKX/rUvwRygmKZoyz+EysOS+4qN4r/BLle49BJIWCI/levvELG31lnaf97\nYiJZI/PmRdfvHG1CFf+pGTuWXLDq8eC2qamhJA6jFq3du5OJaGUVZpHNm8lh0KdPePafkUEvMVq1\nHGJEbDTTcAWxHhz/DkAW5/xkAE8BmKe3Ief8Bc75YM754K5WrvSA7plQVUkWg9XdZbbZj9KmdGDo\nUGv/qIVimaO2OACLAfK4OLJz3ajg7d+fbrdutb+PSZOAv//d/3dKii+BYNcu4PPPKQXXqPhy8mTS\n/Hm6Z0bLR7RTNxsIHjWK3lPX3FX9+tE8+lCsXUujh8NEURGJp9NOuHokJNB7HC2LY948sqb69YvO\n8yuJpnCUAVAufTO99/ngnB/knB/2/r4AQAJjrIvrR6LjMqnq2BeADeFo2IEyZFBLUqd4j+14SSkq\nKvwWhxAOSxbHiBFU/eWGu6BtW/KT/eUvzvYjGuANGUJpvt4EgtdfJ0+fqN3Q47TTaBdvvOHsMJoz\neu3U9ejUCRg+3EXhuO02XxzOkOuuc8cK12Hz5vC5qQTRquWoqaH+VLFgbQDRFY41APowxnIYY4kA\nJgL4ULkBYyyNMfo6MMaGgo7X/VFiOi6TqhGXgDHjmJ8WmTUbsZtloLG1C41y0tMBxlBetB+A3+IQ\nq8uo1XIApKhOJ8hceSUJ0MSJlBpUUQHO6Tp01lmh/fZxcbSLxYttdgtuAZhNxVUydiwZAJZiZE7J\nzg5bLUdjI7WgCbdwRKuW45NP6DXGQnwDiKJwcM4bANwC4DMARQDe5Zz/yBibxhib5t3sEgCbGGMb\nAPwXwETOw1ArLFqGi9VvfDzwwguoSjsJnTtbbFt86BAyyteigce786VMSADWr0fp+VR1KyyOxETq\noWXJVXXVVXSVdYvFi2mfTnPzu3b1z6Retw6rVpEHzOzUtsmT6RDeecfZYTRHQrVT10Mk+y1a5MJB\n5Of7ao0MycoKW4Bgxw5KS46EcOzc6TDvJUTRqxaFhbRojNLo9iCiGuPgnC/gnPflnPfmnM/w3vcc\n5/w57++VazE/AAAgAElEQVRPc84HcM4Hcs6Hc85Xhu1gJk2iWotnnqHA7/nnWy7+AwDExyPzzssB\naEwCtMvJJ6PsAOWjKhOiLFePb9xIaUpusX07+ZTsriLr6ym6/fnnVInOGLB2LV55hQzASy81t5vc\nXOCUU36d7qpQ7dT1yM+nc7uw0IUZ7iUl5vLCs7Op2dqBAw6eTJuiIroVobdw0aMHZbWLDGTLmCh6\nVXP0KFX7jx9vvtlquImRw4ghpk0DNmwAOnZEZaUN4UhJQeblNHzJNeH4/HOUFnwFILA3oeUiwNJS\ndzKqBE4zq378EXj/ffKXtG0L3HMPjg0cjrffpoK/tm3N72ryZMoSchKrb45YScVVEhcH/Pa35AJx\nNMO9oYGEQOHPLS/XESNh0YfBXSVSccMdOHZcy2Gja/a775JYjRhh8znDgBQONULS6+rsWRyvvYbM\nWuoC6tq0tYULUfbJeqSm8oD+NJbajhw9ClRXu1PDIRDLO7vCITJsBg2i2xkzMP/YeTh40LybSjBx\n4q+zY25QKq5JN0hKCllodXUOZ7jvp9ibUjgeflhHjEaOBL78Miy1HJs303fV7Y64ahzXctjomv2P\nf9DtF1/YfM4wIIVDi/nzgS5dUFXRaC3tvKYGuOYadFn0JhITXbQ4srJQ2piGjLSmgFh0ejqt6kyF\nGNyYw6GmUyeK0jsRjrZt/V12m5rwyrNH0COzCWefbW1XGRlUMfxr65grhKNnT1hyg5SUUL8vge2m\ngIqqcdGhdvZsHTHq2pXEQ6sNgEMikVEFuDBCNkTRqxLxfm7ZQn8//7z7HX/tIoVDi9xcNB4+ipr9\ncdYsjq++AjhH3KhzkJ7uonD06IEyZCCzU6CJ6/GQa9lUvSNj5P/J1erq4oD8fPvNotato//3Wnll\n89di8bJkXH36L7Z8uZMnk+vm22/tHU5zJKCdugU3iMfjr8Vz1BQwKQmYMgXo1w8lJYFxKU0xKiyk\n74nLbN4c/vgGQHWMCQkOLI4QRa9KSkqoFb5yM7c7/tolPtoHEJP06YPq/meCFzFrwvHFF/TpDh3q\nH+jkBllZKEVnnNl6PwC/419Zy9G9e4h99OlD8QS3+fhjeym5nFPV2umn++5644eBaEIrXNXtUwDW\ny38nTABuvJGsjmHDrB9ScyQgo8qiG6SiglrkDBtGRp+t1O7sbN/AFA/8nl7GdMTor38l16SLDvu9\ne+knEhZHXBx5e20Lx6RJtNC6+WYS9e7dgccf12yAKvrRAfRVCUfHX7tIi0OHqhGXAAC6Juw3/09f\nfEFFf4mJyMx0L8bRlOG1OBID83sttR0Jl//Gbh0HY7Ty9K60OAdeeSsJp6d8hz7b7OWItm9PHXPf\neceCEWQjNTKWCBAOC24QgJoAnnwyXZBsNwVU+UmV5/w11+gEyF0Ojoe7R5Uax7Uc11xDo3anTKHv\ngU6KPOdkQWdnU91uODr+2kUKhw5VgyjRvevPK8z9Q00NVSCdcw4A/whZN67XVeiKBiQgY1x+wP2W\n2o7cdJOhLa+bCROK4mL6R4fuh2+/pQvANQO/d9SWYvJkct0tWWJiYxupkbFEUDt1C24Qgcfj8GL0\n9NP0HN5Yx0030d2cA+edpyFGYRjoFA3hcFSOIlKXhwyhN19nhfndd5RNdf/91NU4HB1/7SKFQ4eq\ndpTf2PUsk47TTp3oy3PDDQAoWFtb6yDfW0FpGa3qM3sEru4tVY/v3Gk42V43EyYU7doBX39N04Gs\ncNddvtmX5eXUSiEpCbj0onq6w9aUKqqI7tjRZE2HjdTIWGL7drpA+1JxJ00C/vUv/wZt24YcHpaW\n5rD7QE0NXd3atwfg31fr1jotTbKzaaO6OgdPGsjmzXRqW5nK7ISsLLrWWxppoGTmTLpenHwy/a3T\nbbKwkAzhceNsPk8YkcKhQ9Veukh3HWYhdbBtW98XyDfQyYU4h68b+mcvBdyfnEznn6kvvk4NR8hM\nmFCIHEirmVVff00XHNCKqrycDq/9Zb+lqYdGLboNEB1zCwuBM84IsZq2kRoZS2h2xc3L8/9+2mkh\nh4d5PFQTeuSIzYOoqaEZpt72CuXldF5ecAEVrQVl/IlaDtcCgFT816+fxQ4PDujRg8pXbLe4KSmh\nVdKwYdSlwkA4zjqLOkTEGlI4dBDtQjqvW2QuTeeKKwJ6XvgGOrkQ5/BN/pv/bNBjpms5du3SFI6S\nEqqBEFjO3GCMMrVE6a4Z6uuBDRuQsmwRGANefJHuLi4GWK8cpEya4Chlc/Jk0qSVK0NYUBZjArGG\npnCI3M0PPzTVeFBYrbbdVTU1QcV/Hg9ZfhUVwPr1qu3Hj6cPWgiIC0QqFVfguJZDBKaSk8nq0Li+\n/PwzrcVipamhGikcOlRVAR07ciRcdzUwa5bxxjt20AxTxRLEbYsjPq4R3fZsDJq+Y6rtyJEjNIBB\no/jP4/HHt22nZebmUhW42YDOjz8CdXUo+e/HuPJKWnQBZOVMmgRsm7/Rdp/0lBT//A7OQ1hQM2YE\nB/dDxARiCc126lu20Gr2/PNNfYi2uiwr0REO0QsryF3VsSP51uLdSeisraVFTjSEw7ZhqsxoeO45\nKtBQUVhIt7HS1FCNFA4dqqqAbt0YfXILF/rcKpp8+SXdegPjAH1n4+LcEY7SUsDT/hha8YYg88JU\n25H6euD22wNSX5WIWOVvfmMzc+O00+ifjd4jJd7gt2fUALRrR1rIGLm927UD0goepwOxkVlQUoIA\nMUpKMrCgRHNLbwdiZGeHjAnEEprt1LdsodzaqioqOQ7Rg0UIh22L44ILAt6v8nI697t3pxIdzTjH\nv/9NfiwX2LqVTpNICoejtiPHj9M/CuEYMkSzX8y8eZS1HKvGrxQOHXztRiZMoBW70WDrL76gjQf4\nBxTGx9MXyC2LIzPNm1+qOluFxWF4je3Qgb6sOqP0rriCbo8etZm5cdVV1PRIndGjh8dDgYgTTkBF\nBS1CR4xQiNbgwWS92QiQezwkPk1NfjFKSjJYfF93HQXq8/Mp2txMRAPQ6Yr7v/9Rc6PDh4F77gGW\nLTPch+P2/DfdRIsSL3v2+MVo7Fjgm2/I2A1g1iw6RhcIe0aVRrp2hw4U/LclHHV1wJ13wtcaobaW\nrI5Vq3yb7N5Nf8aqmwqQwqGLTzhGjqQLr97VlHMSjnPOCXJ7uFUEWFoKZPSIoytgdeA4kvR0Miiq\njaaUHD5saA2IRanjHHGzFsL559OFIy4OH3xA351TT1WIlugdvXatrcOoqCARevVV+ltX8//+d7Iw\njhwhK6i21tbzRQPddurdupHrMCeH/Fghkha6dCEXpW3hOHDAFwGvrSWRUApHU5PG+5+d7VoCQlER\nfe369nVld4HopGuzNwvsp+S2bUtZVSNH0t/x8dSS/u23fZuIEbGx6qYCpHDo4hOOhATKh1uzRvvC\neOgQWRpjxwY95EYRIOckHJn929HF/8ILAx43Vcvx73/TEkknBbKYejKiosLBaI1Bg4A//Sn0do2N\nAUvQigp6WQEXwIEDaYVns55j7lwSoSlTKGNr166A7yVRVwc8+SRFb8Wb2IwmQYl26gFejrIy4JFH\nyHJq1YqW4SGEIy6O3Eq2Fg1NTRTfuP9+AP59iLdz2DCyJoPcVVaLAA2KNDdvpt2ZNXYtYZCubbsI\ncN8++uAE8fFk7SoyqwoLqdGD292B3EQKhwZNTbSC97Ub+e9/aZaFVpV0u3bAZ59ptnN1w+I4eJAW\nxBmZTPP5TQU3S0tpaakzjFkIR2Ojg7qTpCRg06bQ223aRBcbb/BbMzMoNZXE2KbFoeTvf6cL2I03\nqr7oy5bRGzt2rAupRZFHtFMPeN/WrQP+7//8r0MkLYTA8lwXwcGDfvGAfx/inIyPpyLAoLRcMdDJ\nzColRJFmWDOqDNK1bY+Qfewx+i4qX/uQIVQH1dCAffsoZHrxxc6Ha4YTKRwa7NtHF1GfcCjy1IMw\ncG9kZNB36+BB+8cihCczEzSv+b77Ah431XZEJxUXoMD0tm3+lavta2durrlaDiEG3uWUpnAAtOx6\n7z2bB+MnPp6KAevrVcMKFy6koo9zzmmWwmGYiiuGUuTm0mtSr5pV2K4eV3TGBYKFAyBd3rNHlZab\nnU1BYjPdOQ1W/U1N9JLDJhwG6do9etDrslzHWFJCbkRlF88hQ8js/vFHfPIJfSdjOb4BRFk4GGNj\nGGNbGGPFjLG7NR5njLH/eh/fyBjL19qP24gajoAGh3PmAMOHB64UmprIdFZdzAVu1HL4iv8yQRfd\njz4KeNyUq6q0VHcOx86ddFE980z625FwVFaSD8UIVSv1gLbgSnr3JveaC5xwAhmNS5dSPzkAJBwj\nRtBzZGQAo0dbmxwVZTTfty1b6KTt2JH+vv12couE8OPYrh7XEQ5lIoJmWu6119KFMmRnThiu+ktL\nSUPC1hV3xoxgK92bri3WYZa/20I4lAwZQrdFRSgspO/00KG2jjhiRE04GGOtADwDYCyAXABXMMbU\nXr2xoDapfQBMBTA7EscmFkIBwpGYCKxeHVjluWkT+cXFPAkV4lp9+eX2L8i+4r8MaHZXS0mhYvWQ\nriodi0O4qc6goYX2hUN8e0MVAqpaqZeU0GsL6oaybx9lBa0w2SssBNdeS6u46dOB9atq6cMR0cfu\n3Wn4tiKdOtYJaKcu2LIlcARe69YUowuBx0OaryoRCo1KOPbsoY9V+b3RTMtNTTVsfxOAEEE1WVm+\nUy1sFsekSdQVQYiHooWLrSJA0bVQbV6fcAKwbx+OjZ+ITz+l0zJWRsTqEc3DGwqgmHNewjk/DuBt\nAONV24wH8BonVgHowBjzqHfkNprCccEF5PdQZleJkVw6U4eEcGzaZH80p1jRpKeDTOfq6iDT3bCW\ng3Ny9OvYvq4Jx8CB5HtWjihU460Y92VNQSczCKALy6xZOoUA1mGMvvNdugBX/iEZx+Yv8nfka4Zo\nvm/btwfPTr3zTnrhBqSl0Wliaq6Lkp49KTDuPRDR3l/t1Q1Ky21oIGtIpA/pUVxMySfqq6h31R/2\nVNytW8mU/+c/ydrfts2Xrm2rlmPfPvJbqz84xoAOHbBoEX21Y91NBURXODIAKN/2Uu99VrcBADDG\npjLG1jLG1lZZ/gYEIv49YPpfhw60Ip07159d9cUXtFrQ6QEl4gYhK5gNKC0lAUtKgm6vA8O2I4xR\nup9yIoyC4mL6HvbtS8dmWzjS06kC9qST9LdpaCCf0WWX+e7SFY6UFAqQO+iUq6ZLF+rCUVQE/O1v\nqgfHjvUXtDQDdIXjiScC71u8mNqPGGC7erxvX4q7eXcgqsbVBKXlxsfTB2HUwrihgYJSrVtT9ptw\nI2Zl+Vb9mzeTQWJ5vLNZhLCNH09Zg4q5tLYsjvh44KmngFGjgh/74gvMu2UJOnTgvkzdWEZXOBhj\nCxhjPSN3KM7gnL/AOR/MOR/c1eGZJIQjqLnYhAl0pd20iU7sr77SdW+ICmaRGWF3eldZmSI8kZND\nQnXoUMA2hlkx+/bRMev4IbZupV0yRitPR/HhpibjHaSkkFXideDW1tLr0x1BPXgwrfRcnCVy3og6\n3JbwLJ56SlW83NgYG6PVTCDaqQcVHLdqFdwc0kRmle3q8b17A/5JTziGDaN1V4DxKDKr9PjnP8lM\neeYZ4NZbqX0zQK5i76pfZFSFLfto/nyypLOz6ckeftj33UtNJQ+dpVqOdu2AW27xd8VV0LDvED4q\nPQUXDNtrxrsYdYwsjpcBLGKMTWeMheOllAFQLtUzvfdZ3cZ1qqoobpCYqHpg/HgawpKQQG6XRx+l\nVZEGooKZc4NpaCYoLfXGNwBqGbJ1a4CrB/C7qjSvr/PmUVK4zhleXOwP0TgWjuuuCzq2ANasoe5t\nXrZvp1td4Rg0iC5OjqbmqFi2DI/V/wUDsg7i2msV7hnHLz5yiHbqAe/bokVU9XjgQODGubn0Dwbt\nb21Xjz/6KJ1b8P+/1vmtmZZrNJejqYmskUsv9VuBvXvTl1LxGRUVhTEwfuwYnavjvd7zzZspCUYR\nw7OckltSohsDXNZ4GqrRBRelN4+5x7rCwTl/D0A+gHYA1jLG7mSM/Vn8uPDcawD0YYzlMMYSAUwE\noLapPwRwlTe7ajiAA5xzJ9MDTOEr/lOTlga8/DItc1JSaPyjTv8ngOLmgwfTl/wPf7B3XQqwOHTw\neGgVGtTaAVBF1wNpbKRz2TXh6NePDlh98RLccotvXglgkIorGDyYlqpuDv5ZuBApiU0oeCcBNTXA\n9deTm2/Elw9gTzkP36REF9F835YuBV56KTiDSrTBEQEBDWxnIysaHDY2UoBdy+IA/Gm5GzZ47zAq\nAoyLI7/WnDl+c+L88+kE967W9+2j71fY4hspKXQu33UX/a2R/GG5CHDmTN2RuYXLuyIZxzDmWKHN\nA44soWIcxwEcAZAEGnat/HEE57wBwC0APgNQBOBdzvmPjLFpjLFp3s0WACgBUAzgRQARiWbqCgdA\nhUfp6f7hwwbT4ubO9WfqXnut9R5QtbW04A4QjiuvpGC3AkMf9a5dFKzRKP4rLaV0eteEQ5S6aq2q\nRGB80CDfXaaEo6bGnyvsBt403IHDU/Doo+SNuPJKYHlZTzx0/G/6ohdD6NZw9O4dnEWVm0tXOIPK\nzuRk0mfLFodCOKqqyFDQE46gtNzsbDLp1TUar79OCtSqVWBr/bi4AJ+UKFkJa3PD+Hj/MYj31olw\n6AT0OCfHwHndN6L198sdHnRkMIpxjAGwHkAqgHzO+f2c8wfFjxtPzjlfwDnvyznvzTmf4b3vOc75\nc97fOef8Zu/jJ3HOnZcSm0BXOAoKaIkq/EJlZSFHjZ56Kt1+95314xAZVQHGQkkJpQUrMCwCNCj+\nEz2qhLchLY2StkzP61YjhEOrEPCnn8gsUglHSopBOr/qYuGY7dvpi+9tDyP096uvgCYeh9m4Caxj\nB8sJDJFGt526OqMKoCvrzp1Up2KArepxhXBoFf8pSUuj74JPOO66i0wGpYX09dfUgWHmTO2d3H47\ntVRBmJsbHj1KA7GUK734ePqiqIRD3UHEEB3hWLeOvqYXj9xHXwbLedGRx8jimA7gUs753Zxz49LT\nFkZlpY5wTJ8e3CwwxKjRjAzal9XJqoCq+E+gscwxLAI0KP4TqbhKiwPwF0BapmdP/cZ6omJcJRxB\nbcHVvPwyWRxuuJDataOsFm++Y0lJYCO55GR7CQyRJuh9a2ykD1NLOExiq3rcgnAA/rTc/fsR/KEf\nPEii0asX8KDOuvT7730ZDZs3k8GirqVzhSVLKKFAXRDav7+/1wsspuQ2NJBrTkM4CgvJwBr3zBha\nxbg0qyScGMU4zuSch25008LgnNxDmsJhY9QoY7TSsmNxaIYnRDaK4kJq6KqaMUO3+WBxMV0shcXi\nuPNGq1bUUFGrraeqYhwwSMVVUldHw9DdiHN06kRxFm+5tcdDr1lcw2prOY4carKcwBBpgt63vXvp\nxehFih9/XDsFVIGt6vF776XgHcwLR2OjNy330CHg97+nMcEAcMcddF6/9pr+9MfevX1+uqIiMgDC\nco2dP58WGep4xP/+pwjSWEzJ3bWLXryOcJx1liLbtxnE2WK8PjHy7N9PiwNN4bA5ajQ/nzJ4rfa1\n0bU4jh0L8Fm3aUPXZE2L48ILdVOGi4vpuyjqq1xp2TRtmr+aUMl99wELFvieTLctuBphoThteFhX\nR33WVf3nKyqoAeKCdw+hPQ7gw4+Ajz929lThRLxvAam43buTmXTttdr/dOQI1RwZZFYJi8PSNWvy\nZJ/bTwiHUReR4cMVabmtW1P7nDVr6A2fM4eKa047TX8HvXrREx09Gr7mho2NdFznnx+cVqnqWWdJ\nOLp0oUCGSsC3bCER9BX9XXIJva8xjhQOFZrFf4IZM4KzVkyMGs3PJzEy0ag0gNJSEoQAi3nPHgp0\nd+0a0GI6yEddUEBndlwcCZtGHGbr1oBsSt+X3pFwHDxIvmp188e0tABBqaqi61hI4TjpJFpWOi0E\nXLaMUqm/+SbgbtGCfewlbbA1Phf53ctw0UW08I1FqqrIpx7yfVOSm0uKICLKGqSl0XrEdEPO+noy\no/fvB0DnTMeOxp1ElGm5/M236JhmziTl/t3vqJjQCK9aHt+yDb/8EibhWLWK3uTx6iYWoEXHtGmU\nwQbyBDBmspajbVvap2qRKSYk+4z0+HiysGMcKRwqNNuNCMSo0exsS6NG7QbIg1JxCwrIR19XF9Ri\nOqDtiGhFXVpK2+3aFRTEb2oid62yzZYrwrFkCZn4SpXcto3cJYp5FyEzqgTJySQeTi2OBQtIcHXa\nw4AxdPXE44tzZmDkSHK3+xoixhCa79uDDxqvUkVKrsHKxXL1eHk5WYNeV5Ne8Z+asWNp2w3XP+0P\nApeWUi/xUN2Q+/UDTjoJv2xpQGNjmISjTRsa5KIxXwcpKfR9//prAJRk5fGYtDhWr/a3KFJQWEhv\noy9/ZcgQUiLbgcbIIIVDhaFwACQS27fTldfkqNFevchlajVAHlD8B+i3mL7jDngad2H3bq+f4a67\ndFtRC8rKSH+UwiHSMl1JyVUGyL/8knomKVJdRQDa1Mp5/HhHgV8Agd1w9UhLQ9vq7fjkE/IY3Hkn\ncPfdseVy1hSOr74KCNoGccIJdJUzaHtvuXpcozOuGeHwpeXWjgx8IESSCQAy3TduRFHiQABhKv4b\nOJDMzfbtgx9LTaXFop2U3CeeIGtFQVkZ6UlAbyrRKVfZTDUGkcKhIqRw2CAuDjjlFBcsDj2buKoK\nnmXv+qvH9ZaNiv9XZ1QJHNdyiHx35UVKJzAOaLRT1+L++4Gnn7Z/TNu2URqO1ipSiffFJyXRxMBp\n08iTct11sZMhqdtO3UhYExKoCltkQWhguXrcpnCkpQGn4jsswPnBD5rs3yFScZ2uJYKoqKDz1mil\nkJtrTzg0AnqiFVaAcIjO0VaFw2BKYjiQwqEiHMIB0PmwYQPF3szQ0EBfxgDh0AvCezxIv2Mijh1j\n5KPWqBJX/78QDmWMA3BBOBISqPmdUjjWrg1opQ7otAU3gnOqVrSDaM0eSjguvdTn8mnVCnj2WRqo\nN2cOPbRtGxkt0exMEvS+HTpEWRGhrqIFBdTzSQcnFgfn5oUDAMa2W4lv8Bvsh2pVHyLJBABw/fXY\n/NIKZGbqJ1/Z5uWXya1npJ79+5NQe7/IYvZ4SKtUJRzl5WRg9eqlspzatKEsSKNmoWpCTEkMB1I4\nVFRW0mdndlyAWfLzKfhoEJ8MoKKCzs0ADdALzs+aBc9g2nD3btAyOUQQf+tWcvmrSzxcadnUv79f\nODQqxgGTGVWCpia6qOgMzArJ5Mn0Zerb13i7KVPIP+WFMWqH/5//UBDz9NMpbmm3Rb4bBL1voveX\nmeV3U5PuuNYOHeh8sGNx7N9Pbk+zacxjb+uLRsRjMRRFiSaSTAAA+/djc1nb8MQ35s+n89TAMsOA\nAaSQ3oFlWVlBSY7B7N9PGyg+uL//ne5u106jjunf/6ZUZbMYTEkMF1I4VBi2G3GA1QC5ZiquQXA+\nILhpIohfXEznsXrUQVpaQAzbHvfcQ/NaAbrSNTQENT+0JBxxcRS5dxIgz8oKXYXe1EQngKp0XrRg\nLy+nTey2yHeDoFTchgbKVhMBcD0WL6ar1MaNmg+L7simhePss6k1eteuvoWGWYtj+H3noUNqHRa2\nvtRSkgkA8F69sbmuJ07sZ2JeuRX27KGAg1Y2lZJrr6UPwZtJYiolVxHQS0mhlzxnDt21fr3OuVRR\nYZg+HYCN+jKnSOFQES7hOPFEsmLMBsh1exPqBOeD2o6ECOIru+IqSUsj74fZc1aT/HzqpQ3QSvjw\n4QBH7vHj9EWzlFI6eDDFSqxGqr/6iiLd4g01Yv58ysNWZR+JFvmiDVRKSnQqzEU79YD3bdgwSjUO\nZXGkp9OHapBZZcna7NOHUs8SE00V/ymJjwdG/y4Jn7a7DLzRfJIJAOzufBIOoR36p+03eaAm+egj\nOrdCCYcKIRyG1+j+/WnRc/bZvnNJLNg0xy18/z19GAsWmDsIm/VlTpDCoSJcwhEfTwkbjiwOA0zN\nHvfCOQmHOr4B+FNyHVkdtbXAm2/6V7fJyQG+P+GKtSQcgwaRbS+iw2aZP58KzLxBXEN0KiBFi3wR\nILfbIt8pmu3UzdKnDwVuQmRWmbY4Nm701dZYFQ7An5Y7eLA11+hmRgGBE1Nc7JgM0DnSs6e52MK1\n11LVPEy2HUlOpvO3Y0d4PPQxNDXRNUHzXMrNpVWK2QC5qukpAPOuP5tI4VBRVaVT/OcCp55Kiwkz\ni+bSUipcDRompUPbtpRpauaLv3s3+WX1LA7AYZyDMYoXvP8+tfhQjS41XcOhRLi6rBYCijRcM1F4\ngxdfUUELbICKm6MRINd8304/ndr7hyIxkcQjhHCYfl0PPEAFlbAnHCIt9/vvrcWMNtdRc6oTT3A5\nze3116ka1ExTzR07KMUcdK1ISAghHB9+SGl6XsTi8YMPKHMv6D1PSqJVplnhuO46mo0iKhItuP7s\nIoVDgZi7HK5RlPn5VMpgxsVRVuY/D8zAWIgRsgr0UnEBl4QjKYl2vmED9fdRDG8CbArHgAEUuNYy\nk/Qwm4YrMKiAnDuXkm66diW3o9UW+W4Q9L41NZGTPGjimA4DBoR0VVVXm0xeUzU4TEkJ7gmoR0qK\n37Vqdazy5oqOaNsW8Fw4xNyTmaVdO38gMhT9+1NKLue+6QqGwvHss75qUpEceOaZ1A3omWd0zqUh\nQ2iRpJPM4EO4Ju65h1abFurLnCCFQ8GhQ/Shhks4rATIg4r/TGDW1RB24QDI3P7ww6BW6oBOW/BQ\nJCYCs2aZ/3ID/h7eZoUjNZUuIAYvPi+P+o5Fg6D3rayMsmfMFjSIdGMdk1dYDKbclCrh8HjML3LU\nMaOkJPMxo82b6brNuIvB8cceI/UyS//+tAL0niciJVcXRSbIypWU0ejtDanPkCF0QTJKw9yxg1Yx\nT/RC2soAACAASURBVD5p/thdQgqHgnDVcAjy8sivaSZAbmbyn5qAtiMGFBfTl1ZrTEfXrhS4c0U4\nBBrCkZMTnNEVkldfpStUqCInUQx18820jLWSjTVjBjBunO7DeXm0aA+1EAwHP/1EL913YRcXFbPC\ncfnllNKsc4W3VASoEI49e6y5qUTMqLGRDqWujtYFZhYSRUXAiXuXA7/5jfknNKKhAfjXv+iKbhbV\nNEDDEbKNjWQBeIVjzhxK97/kkhDPMXo0mbh6XSM5p1oNQFVBGBmkcCgIt3AkJ5O3IJTFwbnhGA1d\nzLqqtm6l81irJXWrVvT6HQuHco7t6NEBF3lLqbgCUeQkWrjqFTkpi6EACuZYKYa65Rbg3HN1H87L\noySxMGY66vLtt2Rg+GICVoUDoM/FW4OgxlK/Kg2LwwoVFeTff+stEo9Fi0LH/g4dogXViV2rg9yf\ntlmxgl6L1igAPXJzKQXaK8A9etBxaRb3lpVRenevXjh8GHjnHeCyy0wUL2ZmUgxJL6njlVfoTZs5\nk2IakYZzHvEfAJ0ALAaw1XvbUWe77QB+AE0iXGt2/4MGDeJ2mD+fc4Dzb7+19e+muOYazrt147yp\nSX+bvXvpOP79b2v7njmT/u/gQePtBg7k/He/M3583Dhrzx3AG29wnpLCvRO86Sc1lfM33uBNTZy3\na8f5LbdY3Gd2duD+xE92Nj3+4YecFxRw3qWL8Xah2LuX802bdB9esYJ299FHFo/fAcnJ2i8pOaGB\n86uuMj6ZlNTVcd6qFefTp2s+XFpK+33uuRD7aWrifNEizn/6iXPOefv2nN96q4UXpGLGDHreggLj\n7dasoe3mXj2Pfqmutv+kgjvu4DwpifNDh2zv4tln6XDKyjQe/PJLenDJEj5nDv26fLnJHW/dyvm8\necH3l5XRm37WWZw3Nto+bjVWrrHRsjjuBvA557wPgM+9f+txNuf8FM75YINtXCHcFgdAAfLKSuNV\nndVUXIHhCFkvIhVXK74hcFwEaDApsaaGWndbtjhCFTn99a/kKNdZTZs2ER56yHAmhKizi2Sco6QE\nOPlk/9++3P+drch9Zza4kJhIH7xOZlW3brSrkBYHY2RF9u+PY8fI3W/V4lDy17/SW37zzcZBZt+4\n2EHeRpVGjR3NwDmla597rr3+JV4TybCWY8QI+sKffjpefpmaFxiNHAnguefIvajOVvjxR3LB/u9/\nNvy97hAt4RgP4FXv768CsGAnho9ICIeZALlu8V8IzNRyiILUUMLhyFVlcJG3lVEFhC5y+vRTurLo\nXcHMFkOlpZGyqVs4eGnfni4UkRSOjRsDS2J8uf9J+4z/UQuDzKqEBEr/DvnZV1dTLmllpU9knNS0\nxMdTQ9r6eiqR0IsfFRXRtiec7vX7W63pUXPoEJ2IIQMOGtx3ny/Dz7CWgzGga1f8vDMZy5ZRUNys\nzmPIEAoAqU+20aMpbmIlw9BloiUc3TnnYl2zB4De3DAOYAljbB1jbKrRDhljUxljaxlja6uEAlik\nqoqE3KjztlMGDqQTxyhAbtfiMOOj3rqVbo3Oue7dbUyDU2JwkbctHKGGaGVnk69/1ixbw7Z8iCug\ngcnlWmaViY6m27dTBlK7dsD119OcoWnTgD1lDeT//sc/rD1nbi6ZnDrjKE21HfnxR7rY/vCDrRoO\nLXr3phZNn3+u3wh582Zv8+V+vShu5aQyuqCAzLjPP6d5JlYbArZpQxbPgQPGbUdmzwaefhqvvEIf\n85QpFp5D3WK9qgp46SX6YiYlWTtetzHr07L6A2AJgE0aP+MB7Fdtu09nHxne224ANgA4y8xz241x\nTJli3hXuhH79OL/oIv3H77uPc8Y4P37c2n737SMf6uOP628j/KzFxfrbPPEEbVNTY+35fbzxBsU0\nNGIcjz5Kf9pyKb/xBn1AjNHtG284206LBQvoAFeu1N3krrs4T0zkvL7ewrFrHaPOeyQ4dozzQYPI\nnb11q+r/16+n/3n7bWvP++ab9H8bN2o+fN55nA8ZEmIfhYW0j+++4++/T7+uX2/tMLRoauL8ggso\npuMNnwSQm8v5+PHOn8fMex+SDz+k//vmG97UxHnr1pzffrvGdsOG8YZzz+Pp6cZxRU2amjjv3Jnz\nP/6R/r78cjrxjL68DkAsxDg456M453kaP/MBVDDGPADgvdUcd8U5L/PeVgIoBDA0XMcL0Iqhujr8\nVcGnnmrsqioro5WfyHM3S/v25MowclUVF5O5b5SI4biWw6DJYkkJ+dJttcQ2O0TLxrAtHyYtjuPH\n/fUwtjDR0fTWW6kG7LXXNFyLIqPKapvYM88Enn9e17dkqnpc0RnXLYsDoFPlxRfp3Jg8OdC139BA\n1rLv5TY22p+S50Y3WZGS+9NPYMyglqOkBIsSfofdu03UbqhhjAKXr71G5so771DVYECXy+gQLVfV\nhwC8DRxwNYD56g0YY60ZY23F7wDOA1ksYWPTJkq1DHfb7Px8Osmqq7Uft1P8B/irx41cDcXF5BXR\nSsUVuFIEqHPxtpWKG0l69yZ3gEGhYV4e3TpyV4UI9v/vf/QzfTpdK4IQwmHVz52ZSW4enUCeiG8Z\nuilVwhEfb741TijS0miN8d13wMMP++/fto1iID7h+OMfgzoum8aNbrI5OeQuMqrlOHQIqKrCnLLz\n0KULcMEFFo+zoIDUsr7e/4EsWBD2IU1miJZw/APAaMbYVgCjvH+DMZbOGBMtIbsDWM4Y2wDgWwCf\ncM4/DcfBiFbHIiEn3G2z8/PpVi/OYaf4T5CebmxxbN0a+lrjWvW4BjEvHO3a0dLQwCTr35/OD0fC\nYRAHWruWyklGjyb3uyZbttA+TE/CUvDzz/7hVio8HrpOGc6XqK4mtWjTBuXlFBNzM7nn4ouphOHR\nRymmA/iH7vmGHuXk0ApLJ1ZjiBvdZFu1Am67zSdempMAt23DXnTG/J/6YvJk851hfEyfTpkQSsI8\nZ8MsUREOznk15/xcznkfr0urxnv/bs75+d7fSzjnA70/AzjnYWv1WFICXHGF/2/NVscuEiqzyk7x\nn8DI4jCTiguETzjq62lRF9PCAVD/pw0bdB9OSaH30JFwaAX7U1Kw9+5/4fe/p4vxm2/S9UmTyy+3\nfwH5298o0q6BqerxW24Bvv4aYAzl5eHpEvyf/9DFeMoUygIMGhfbqxed0Nu3W9/5jBnBSmenm+zM\nmfQ5gI51zx6VjlVWoqDV1ahvjLPupgKiMmfDLLJyHP4WCIyR9RnuttmdOtGCVsviOHKEuofbcVUB\nxm1HqqrIeg4lHB060OrIbeHYuZM8VzEvHFdfHXLaoOPMqkmTgCeeCMiOabzkckwqvAR79lC2q6H7\nZ9w4f8sJq+Tmkump0c3QVPV4Roav5YfVdiNmadeOSlR++YV6W373HcX8fAtw4ee3U8sxYYL/SZx2\nk62qAhoafMaKyIgEAH7uKMwZ8C8MHswtTYL1EYU5G2aRwuGlshK48UYaAqbZ6thl9ALkdlNxBR4P\nlSFoDWIyam6oREyDc/s9sJ2KG2lMvPi8PLr2qj0JljjxRFqiLlwIDB2KB5ecjkWLqGOqofv+yBGy\niuw+eW6uP9qswtTs8cJCancBe+1GzDJiBInGc8/R6N76ekX80YlwNDZSY8DPP3fWTfaDD3yDv7RS\ncr//Hti4keEPfzBbuKEiVAp6FJHC4WXuXPrCDhxo0OrYRfLz6Xt76FDg/XaL/wRGK0YhHGbiqa6M\nkFXR0oSjqcnvQrHFDz/Q7Ukn4eM/zMXD5dfhj3+k8QqGrFlDK49ly+w9ryh/1ygENOWqevhh4Kmn\n0NBAC65wCQcAPPUU3QoXkC/+2LMbXUBNl2EraNOGUtbsBtcF4otUVKQpHHOu/wbJ8fUBbnBLmBgB\nHS2kcESJ/Hxy0apd6U4tDqO2I1u3ks/cTE80UQToJiUl5AITxxizmEgtciWzatMmlLc/EUMvTseV\nd2Vg0CDg6adMVF3aaW6opF8/8vFrtB4RA8EMP3tvg8PKSnqLwikcJSWBXfH98UdGU/hUnZdN8fXX\ngT4lu/TtS++jhnDU1gIF63NxsWcVOnRw8BxOUsvDiBSOKKEXIHfL4tDKrCouJtEwk90RLldVz54G\nAd9YIS2N/P/79eda9+lDPnenwnFf8kysWcNw/Djw/owtSB6cR21wjdiyhSL0dlcXKSnA4sXADTdo\nPhyyetwrHG60GwmFx0PnbFycqt1KGigN0upESM4pxvF//+f84JKTKburqAipqUDnzv649fzCJuxv\nao8/DC9y/jwxiEE2vySceDy0qlcHyMvKgI4d7WVZiv0C+q6qUPENQVoaxf0aG9270Md8Kq7goovI\nnWOQj52QQCEKu8KRkgLU1i73/V1XB+SM6YdkrMOxx6+mYi89tmzxr3btcs45ug8Z1gLV15N/1eXi\nPyNEC/apU8lT4zu2xx6j6XpHjph/L7ZsoXTi00935+DENEAEpuTOef44slCBc0ZGYXBLBJAWR5Rg\nTDtAbrf4T9CpE1kU6i8+5+ZqOARpaWQd22z7pUmzEY6cHOC882hFaYCTzCoxBU80vPO5YG78J81q\nN0oz3bLFvptK8PPPlNVVXx/0kGH1uJiz0qmTb5twC4du/LF3bzJBTA0Q8SLqV9wSjmnTKL0ZfuHY\nuRNY/HUSrsXLiDuhOZzw1pHCEUXy88nNrEyOcVL8B+jPHq+upvbXViwOwD131b595PlpFsJx7Bhl\nDmlkHSnJy6N5UQcPWn8KTxpHYiIJeny8wgVzz7W0ev7Pf/T/efZs4I47rD+pkm+/Bf7yF82+KYau\nqk6d6H+uuCIiripDRGaVlS65K1aQT8mp8Ap+9zvqjwK/cLz6KsA5w9VZS5vJCW8dKRxRJD+fsiKV\nq1YnxX8CLVeD2VRcgdvC0WwyqgDyG02YAHz8seFmIkCuM97CmH/9Cz+9tR4A9WfypYD36EEj4v73\nP/0Yy+jRwPDhNp5UgUFmlUjp1uwsHx9PF2yvq6pzZxsV0W4hTiYrKbkrVlAmlune5iFoaCB/c2kp\nsrJogfT88+QJzNmx1PwXrpkhhSOKqAPk9fXkz3XiqgK0iwClcFigfXsqzDORkgvYdFdt2IBT439A\n27bkogpwwdxzD119tPr7//wz8NFHDgtIQCtuxgxTcjVfflERta7fuzesNRymEFFzK8KxaJH1VvRG\nHDlCK8A33vBlVpWV+WsMWypSOKJITg5do0SAvLycXBduWBxqV1VxMX3HcnLM7aO7d0KK28Jh9vmj\niskKyJ49KTZhSzg2bcISfi5GjtTogpyXRwEQrfbIH3xAXQ81qr4tkZpKH4aGuWRYBLhmDY3sO3Ag\nbO1GTJOYCLz9trUU1exsKoB0i/bt6Q1TpOQCwIbHl/hiHy0RKRxRhDFarAiLw2kqriA9nbwcyumt\nW7dSpwKz819at6acfjeFo0sX8uM3C0wIR1wceXwsC0dDA7b9dAy/HE3HqFE62xw/TivjDz8MvH/L\nFn+PHKcMGODvHqjAsO2IqjNuVC0OALj0UvOt5d9+m/yCbtO/P1JefwFnneW/68Vto8D+OTNsjVKj\njRSOKHPqqTQWtKHBefGfQOuLbyUVV9C9u3vV480mo0pgspDFVmbV1q34vP5MABSu0CQ+nqKsDz4Y\nWIjoRkaVYMwYWmGoJhAaVo/X1ACMgbdrH7Y+VZYoKaEsNDM8/TQwZ477x9C/P0pS83DFFdxnJKbi\nCCYN3BS2RqnRRgpHlMnPJ3f15s3uWRxuCYebRYDNTjhmzgTeey/kZnl5JK6W0pbj47E45wakd6vX\nXyzHxQF//jOZo0uX0n2cuyccBQXAXXdRGhDnlB42dSpQUIAuXah2R/Ozr6kBOnbEvgNxOH48BoTj\nvffI6lD37lFTVwesXeteGq6S/v3hOVKM9q2OoLERSE5qQi2S0a5rUnRdeWFECkeUUQbIy8qoMKxj\nR2f7VLcdqamhH6szf9wSjoYGui41K+Ho14+K7EIgAuQaMWZdmnr3wecHh2DUmATj5J4pU2jg0uOP\n099791LajhvCYTAFr1Ur6t2na3FEsPgvJGabHa5bR+IRDuEYNw747DNUHEzGtGnAqn8uwzQ8hz0N\nnd1/rhhBCkeU6dePxMKb0YeMDOeZguq2I1YzqgRuCceuXVSB3qyE45dfqMOeQdsRwF5m1Yal+1Bd\nDf34hiA5Gbj5ZuCTTygW0bEjKZTtrnkKQsx60C0CfOklYMWK5iccK1fSrZ2miKHIygLOOw9z58dT\noWJuPZ4Z+T7mvtcyq8YBKRxRp1Ur4JRT/BaH0/gGQLn18fH+FaMT4di/33nmZ7NKxRX88APwpz+F\nnObl8dD13IpwLLn0eQAmhAMAbrqJZo42NNCHmpvrTipTiFkPukWAKSlAt27RL/4TiJMqVBHgrl1k\nQYp0QbdZvNjXah6jRgFffunePN0YJCrCwRi7lDH2I2OsiTGm29uYMTaGMbaFMVbMGLs7kscYSU49\nlcYr7NrljnDExQUWARYXkxVj9cItLgpOA+TNUjhMFrIwZjFAfuQIFtfkY0C3SnOr9a5dqW7jpJPo\n9qWXTD5RCELMetDtV/XII0BhYexYHO3b00oplMXxn/9QFkq4uP9+mnX7KyFaFscmABMAfK23AWOs\nFYBnAIwFkAvgCsaYiwnYsUN+PlXq7tjhPDAuUNZyFBdTQXKI1ktBuFUEWFJCi2U3RDFiWHjxQjgM\nurD7qF2/GctwJkYNsdin5L//pfqN664LyICyjZj1ICyPpKSAWQ8eD83aaGxU/d8TTwBffIE9e/wp\n21Fn4UK6cIfCbC66HRTNDjFkCI3XbcFEa+Z4Eed8S4jNhgIo9s4ePw7gbQDjw390kSc/3/+7WxdX\n5Ypx61Z7nQ/cFI5m0U5diYUKyLw86gNmZsTDyvlVqEUKRl1oof1xQQFlWAkUGVCOmDSJ9vX00/T7\npZf6HtJsctnYSL7LWKnhEAwZYnww775LqcfV1eE7hv79SWmrqykOFU6RigFiOcaRAUAxTwul3vs0\nYYxNZYytZYytrXKzpWsEGDCAVuSA/XbqapRtR+yk4gLuVY83u1RcgHz57dubFg7AnLtq8dIExKMe\nIy6z4GufPj146e/NgHKFm28mF5ii6ZRm9fiBA2RWxZpw/PgjtUHR6PQLgOIPq1c7T1c0on9/ul26\nlCpvm90Jb42wCQdjbAljbJPGT1isBs75C5zzwZzzwV27dg3HU4SNxES6RgHAggXu7NPjocVPZSVl\ncdoRjm7d6PZXKRwApbqZmO8s+gWaEY4lh4ZieO+9aNvBgvkVIgPKNdat86XoahYBqqrGox4YF3z7\nLbVB0Xs/VqwAfvMbZ/NLQiGE45NP6LZZnvDmCds7yTkfxTnP0/iZb3IXZQAU3V+Q6b2vRZGSQgFW\nYUV/8IF3prLDVgViNbjcOyvIag0HQILWubOz4Pj+/XS9aZbfo5wcmk8dgs6d6f0OJRw1NcC6LW0x\naorFpXqIDChXWL2aZnC/+y4AnbYjIjU51iwOoy65NTUUewhH/YaSnj3J8jnjjMBjaqHEsqtqDYA+\njLEcxlgigIkAPgzxP80OMdBHuERTUsRMZWf7FUWAX3vTD+x2d3ZayyFeR7P8Hi1YQBXkJjCTWfXl\nwmPgHBh9xjHjDdWEyIByhaFDqajohRcA6MS3Bg8G6utx5Izf4tChGBIOo1qOb76h23ALR1wcpUr3\n7k2Fm9nZ4X2+KBOtdNyLGWOlAH4D4BPG2Gfe+9MZYwsAgHPeAOAWAJ8BKALwLufcQn1u80D0q6uv\np6ynujrFTGWH+wX8wmH3wu1UOJplKq5g0SLTF+e8PGo0G5SFpGDxO/vQFgcxpG65/kZaiAyo7Gwy\nR7OzAzKgXIExCrh/8w3www++EE9QSm58PPbspYBczAhHejqtvLSEIzGRhmMMHRr+41iyBPjiC+C1\n16ynMDYzopVVVcg5z+ScJ3HOu3POf+u9fzfn/HzFdgs4530557055y4ur2ILMVN51SrFQB+HCItj\nwwZK8bUbdP9VC0daGvVAOnIk5KZ5eRQTNbIUl3yTipFYioRT86wfy6RJNE62qYlu3RQNwdVX0wX4\neSpQDKoe/+wz4KabUL6t1vd4TBAXRyeYVhHg6NHA55+7l3VixOzZwEMPBTWNbInEsqvqV4PuTGUH\ndO1K6a9NTfbiGwIhHGZqFLQoKaFpoyL436ywUAEZKrNq2zbgl70dMCr1mxiKKqvo3Bm45BIqNGxs\nDK4eX70amD0b5XupBWzMCAdAq/233w68r7HRlOi7QkGBf2KkqmlkS0QKRwslLs6fTutkemVaGq2k\nQzUf1aPZZlQBlgpZxGwgPeH4/HO6HZ1b5t7Y0nAwaxb53Fq1CrY4amqAdu1QXkkZYTGlf+npwTNs\n164FOnTwtwIJJ9OnBw/XcjNlOsaQwtGCEe4qp8IB2HdXtQjhqKwMuWmbNpSEpSccixdzpLPdOHFo\njE+y8nioJJxzXxGpz9pUdMaNjycDJWZYv556i4mUYYDScBsa/OZgOIlUynSMIIWjBSPqnZx8wZ0U\nATY2kju+2QpHXh5w+DBw0UWmN9cSjqYmsjhG/TYe7OabXD7IMPD990BuLtKaduPoUYW1WVMDdO6M\nPXtIU8NZFmGZsjLqZrxF0ZBixQqKNYgVVDiJRMp0DBFLH73EZcRgKCeWuhOLo7SUFnzNYs64FvHx\ntPo2SV4eXbfUHosNG4DqaoZRV3Zzd951uMjOBrZtg+cHOnF8nz3nQPfusVXDIRApuSJAzjm1Ug93\nGq4gEinTMYQUjhaIKCoUPdfee89+UaET4WjWNRyChx823ZE2L4+E8uefA+9fvJhuRx2Zbz/LIJJ0\n6gRcdhnSVlKWhi9A/sknwCefxKZw9OxJJ7lIyd22jU7aSAlHJFKmYwgpHC0QUVQohCI11X5RYefO\nlJ1lp3q8WafiCubOBeaba3agl1m1ZAkwoP0ueB77U2wHxpVMnQrPMboIqxcNMSkcycmUdy6Eo00b\nmpz4299G7hgikTIdI0jhaIGIosK6Ovo+1dbaLyoU2Vl2LI6SEhKdHj1CbxuzWHjx/frR61UKR20t\nsGwZMCrhq8gEad3i9NPh6Uc51OXlIEtpwgTUvzMXVVUxllElOOEEf1uUbt2oo3CzXrXELlI4Wihu\nFhXaLQL88UcKE4Szm3XYsfDik5JoyJxSOFauJPEYve+95iUcjKHjY39FYnwjyndzipAXFqJiE3We\njjmLAyCfoLAOP/3UVDacxB5SOFoobhYV2hWOlSvJ6nnoIfvPHXUsVkCqM6sWLwbi4znOavyCpvg1\nI9jFFyEtvRX2VDBfmuseRooRk8IhZhPs3w+cfz7w3HPRPZ4WjBQOSUisCocIzosF3+zZ7nT8jQpp\naeQvP2huYl9eHrnoRMHykiXA8BP2oi0ONy+Lw0ta53qUr9zmq0cob6Re+zEpHOvXAxdfDLz+Ogl9\npALjv0KkcEhCkpZGrq+mJnPbl5QA48b5/3YSnI86t91Gq22TPVPy8uiaVVTkbaO+Dhh1eRcKlorB\nHc0IT5uD2FN8iMxWAOXHqSgoJoXj+HFg3jzgxRcp2DRsWLSPqMUihUMSku7dqZjPbKxCzKsGyO/v\nJDgfdSxmQSkzq778kkRk9Hne9MyEhDAcYHjx5HZCeatMmtORnY3yYx0A+AtDYwpRy/HDD+SjNTFL\nRWIPKRySkNip5fj5Z2q0uHq1ex1/o0JVFTBxIvmcTNC7N4nlpk0U32jbFhjy/t/8k+GaGWkehr2N\nnXAcCcDOnSh/dRG6tK2NTQ389FO/0G/d2mIbDMYCUjgkIbEqHP/f3v3HWFXeeRx/f4ISqOBChTpX\n7bY21bIN6U7bKdVUi5uKUWoQadYsaoLZpHaTumFNuj9tst1NTUy7q5vNoi52MTVh25o4bDGyWmxa\n3f5hdSQIKOpaCgoMAtoNkmJA57t/POcyt3Bn5p6Zuffcc+bzSsjcc+Yw+T48zP3e8zzn+T5798Jv\nfgO33z65FX8LMX06/OhHsG1bS5dPm5YWh+/YkXLNFZe/x5n3fAe2bGlzoO1Re/2XABzkQxDB4NFZ\n1I6+1n1vyuvXp2q09YcY3nmn0tVpi+bEYWPKUV0cSMPMACtWtCeejjr77LQYJsct08KFae3Gr34F\nVy7Idjsu2RNVdT2PPgDAIGlS4wA91GJf91V9veOOk/uln1Th6rRFc+KwMeW94+jvT5+6P/GJ9sXU\nMVLux8oWLhx+D+udtm34ZAnVDqX4D5D+EwxSo8Zg91V9nWLVaYtW1NaxfyzpRUlDkvpGuW63pO2S\ntkoa6GSMNmz27PQobSvvnYcPw1NPVeRuo+60HY1G15gjfvD43PSPV9JKj7Xz01vEIDWC7I6Dwe6r\n+jrFqtMWrag7jh3ACuDpFq79o4jojYgRE4y1V54P3Rs3psd2K5U4FixI2bMFM2fCl788fHz/9svQ\nsd8yc9a0NgXXXh+6czWQEsZbnMMJptNz5tvdV/V1ilWnLVpRe47vjIhXxr7SukWriaO/PxUq7e1t\ne0id8+CDLc/u79oFK1cOL2L+wAfgphujnGtYgOmrVjJv9rsMzrqYQdK+FrWvLeu+An5TrDpt0bp9\njiOAJyU9L+nW0S6UdKukAUkDhw4d6lB4U0criePIkfQI6ooV5SkCO9lqtbRWcGioocDk76mca1gy\nPR+ZweCXbubAT9J8R+2GywuOaARTqDpt0dqWOCQ9KWlHkz/X5fgxl0VEL3AN8HVJXxzpwohYGxF9\nEdE3f/78Ccdvv6uVIrGbNqXFu5UapoK0E9YXvtDyPMfJApP//gJ/dt7GtPK6xOp7j9eb35Wrxq2j\nzmjXD46IKyfhZ+zLvh6UtAFYRGvzIjbJenrSyvHjx9PShmb6+9N1l17a2dja7tixVLFx//6W3jVP\njmr961Os2bsanhsEWpsj6UY9PWlnQycOq+vaoSpJZ0maXX8NXEWaVLcC1IdaRqpUfexYuuNYz6UY\nvgAACRdJREFUvrzL9qKeDOPdBnHHjrQTVlfW52hd/Y5j//70jECO3XStoop6HPd6SXuBS4HHJD2R\nnT9P0qbssnOBX0h6AXgWeCwiHi8iXhv7vXPz5lQR9itf6VxMHTPexLF9e3o2t+QTPrVautPcubOk\n9cZs0rVtqGo0EbEB2NDk/H5gafZ6F/CHHQ7NRjDW6vFHHoG5c2Hx4s7F1DH1O4Y8iWNoKN1x3HJL\nW0LqpHrfb9lSygK/1gZVG1SwNhntQ/eJE2n9xrJlpSwAO7YZM+Dyy1NmbNWRI7BoEVxySfvi6pD6\nnMZbb3l+w5JC7jisfEb70P3zn6dN1yr3NFWjp3M+kzFnDvz0p+2JpcMah6ecOAx8x2EtmjEjvRc2\nSxz9/WnCdMmSzsfVtVrcarYMGpOFE4eBE4fl0Gwtx/vvw4YNaYvnUm4N26pvfrP1CZz169PjR1Ja\nRl/y0t6zZw9X83DiMHDisByarR5/5pk0YV7pYSpIj4y1sqdGfV+I+qbje/aUfl+Ieq0y8FNVljhx\nWMuaJY7+/rQgcOnSYmLqmJ4eOHo0/RlNRfeFOCdtNV7Nhx8sNycOa9mpiSMiJY4lS9J+R5XW6m5W\nFd0X4vDh9PWhh4qNw7qDE4e17NQP3Vu3plpylR+mguHEMVa9qlmzmp8v6b4QM2emoap6dd8HH0zH\nlZ7PsjE5cVjLTv3Q3d+fyossW1ZcTB1z4YVw7bUjv2PW5zTuvff0a0q8L8SuXXDjjempOsjKxN9E\nacvE2+Rw4rCWNUscixfDvHnFxdQxF18Mjz4Kn/3s6d9bsybtlbt3L9x8MzzwQGX2hajV0jDk8eMN\nZeLP9iT5VOfEYS1rXD3+8svw0ktTZJhqJBHw7W/DbbfBZz4znEErti/EyTLxz6SveUt2WfV45bi1\nrDFxvJLt37h8eXHxdFxfH3zuc3DffSkpfOMbcM89sGoVfO97w9v+VUzj5odr1hQXh3UP33FYy+bN\nS6MvBw6kN5PPfx4uuKDoqDpoaAjeeCO9vvvulDRWr4Z16yqbNMyaceKwlp1xBsyfD88+CwMDU2yY\nav36VFf8scfSavA5c+D++1PyqNwGJGaj8/94y6WnB554Ir2+/vpiY+mY+mrwd99Nx3v2pDuNWbNK\nv9eG2Xg4cVgu9criCxbARRcVG0vHVHQ1uNl4OXFYLvX1byOtc6ukiq4GNxuvoraO/a6klyVtk7RB\n0pwRrrta0iuSXpP0N52O04bVVxC/+mo6HhiYQiuIR1r1XdLV4GYTVdQdx2ZgYUR8CngV+NtTL5A0\nDVgDXAN8Elgp6ZMdjdJOmtIriO+8c7iueF2JV4ObTVQhiSMifhIR72WHzwDNHupcBLwWEbsi4jjw\nQ+C6TsVov2tKryC+6aa0+rsiq8HNJqob5jj+FPjvJufPB95oON6bnWtK0q2SBiQNHDp0aJJDNJji\nK4grthrcbCLatmpJ0pNAs8+jd0TEj7Nr7gDeAya8y01ErAXWAvT19VVn384u4hXEZgZtTBwRceVo\n35d0C3At8KWIphs07wM+3HB8QXbOzMwKVNRTVVcDfwUsi4jfjnDZc8BFki6UNB34E2Bjp2I0M7Pm\niprj+DdgNrBZ0lZJ9wNIOk/SJoBs8vw24AlgJ/BwRLxYULxmZpYppDJbRHx8hPP7gaUNx5uATZ2K\ny8zMxtYNT1WZmVmJOHGYmVkuav5AU7lJOgTsGedfnwccnsRwila19kD12lS19kD12lS19sDpbfpI\nRMxv5S9WMnFMhKSBiOgrOo7JUrX2QPXaVLX2QPXaVLX2wMTa5KEqMzPLxYnDzMxyceI43dqiA5hk\nVWsPVK9NVWsPVK9NVWsPTKBNnuMwM7NcfMdhZma5OHGYmVkuThyZKm5TK2m3pO1ZPbCBouPJS9I6\nSQcl7Wg490FJmyX9b/Z1bpEx5jVCm74laV/WT1slLR3tZ3QTSR+W9DNJL0l6UdLq7Hxp+2mUNpWy\nnyTNkPSspBey9vxDdn7cfeQ5Dk5uU/sqsIS0YdRzwMqIeKnQwCZI0m6gLyJKuXBJ0heBo8BDEbEw\nO/cd4O2IuCtL8HMj4q+LjDOPEdr0LeBoRPxTkbGNh6QaUIuILZJmA88Dy4FbKGk/jdKmGyhhP0kS\ncFZEHJV0JvALYDWwgnH2ke84Em9T24Ui4mng7VNOXwd8P3v9fdIvdGmM0KbSiojBiNiSvX6HVMn6\nfErcT6O0qZQiOZodnpn9CSbQR04cSa5takskgCclPS/p1qKDmSTnRsRg9voAcG6RwUyiP5e0LRvK\nKs2wTiNJHwU+DfySivTTKW2CkvaTpGmStgIHgc0RMaE+cuKotssiohe4Bvh6NkxSGdnOkVUYa70P\n+BjQCwwC/1xsOPlJmgU8AvxFRBxp/F5Z+6lJm0rbTxHxfvZecAGwSNLCU76fq4+cOJJKblMbEfuy\nrweBDaQhubJ7MxuDro9FHyw4ngmLiDezX+wh4AFK1k/ZuPkjwPqIqO9MX+p+atamsvcTQET8H/Az\n4Gom0EdOHEnltqmVdFY2sYeks4CrgB2j/61S2Aisyl6vAn5cYCyTov7Lm7meEvVTNvH6H8DOiLi7\n4Vul7aeR2lTWfpI0X9Kc7PVM0kNALzOBPvJTVZns0bp/AaYB6yLizoJDmhBJHyPdZUDa6fE/y9Ym\nST8AriCVf34T+Hvgv4CHgd8nlc6/ISJKM9k8QpuuIA1/BLAb+FrD2HNXk3QZ8D/AdmAoO/13pDmB\nUvbTKG1aSQn7SdKnSJPf00g3Cw9HxD9KOodx9pETh5mZ5eKhKjMzy8WJw8zMcnHiMDOzXJw4zMws\nFycOMzPLxYnDrM2yaqu/lvTB7HhudvzRYiMzGx8nDrM2i4g3SOUq7spO3QWsjYjdhQVlNgFex2HW\nAVkJi+eBdcBXgd6IOFFsVGbjc0bRAZhNBRFxQtJfAo8DVzlpWJl5qMqsc64hVVVdONaFZt3MicOs\nAyT1korLXQLcfkrBPLNSceIwa7Os2up9pH0dXge+C5Rq+1GzRk4cZu33VeD1iNicHd8L/IGkxQXG\nZDZufqrKzMxy8R2HmZnl4sRhZma5OHGYmVkuThxmZpaLE4eZmeXixGFmZrk4cZiZWS7/DxQweALr\n/6JZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160fa7fafd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(42)\n",
    "x = np.random.randn(30)\n",
    "y = np.random.randn(30)\n",
    "plt.title(\"Example\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "\n",
    "X, = plt.plot(x, \"r--o\")\n",
    "Y, = plt.plot(y, \"b-*\")\n",
    "plt.legend([X, Y], [\"X\", \"Y\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x160fab4aa20>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Or7+e819/ld5+++00VvMGLmqULvHGG+TGqARtbZzX1Zmva2igbrBsmfvH9+So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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160fa97c0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "a = np.random.randn(30)\n",
    "b = np.random.randn(30)\n",
    "c = np.random.randn(30)\n",
    "d = np.random.randn(30)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(2,2,1)\n",
    "ax2 = fig.add_subplot(2,2,2)\n",
    "ax3 = fig.add_subplot(2,2,3)\n",
    "ax4 = fig.add_subplot(2,2,4)\n",
    "\n",
    "A, = ax1.plot(a, \"r--o\")\n",
    "ax1.legend([A], [\"A\"])\n",
    "B, = ax2.plot(b, \"b-*\")\n",
    "ax2.legend([B], [\"B\"])\n",
    "C, = ax3.plot(c, \"g-.+\")\n",
    "ax3.legend([C], [\"C\"])\n",
    "D, = ax4.plot(d, \"m:x\")\n",
    "ax4.legend([D], [\"D\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4rAi+uFWPh0bXRiXr2cNxzYqQ5eGO9S7LI4IaSVr17MRhTcsXt+rJ8oigRpJWPTtxWNPy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\n9v8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160fa805320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(42)\n",
    "x = np.random.randn(30)\n",
    "y = np.random.randn(30)\n",
    "\n",
    "plt.scatter(x,y, c=\"g\", marker=\"o\", label=\"(X,Y)\")\n",
    "plt.title(\"Example\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.legend(loc=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SvDmSmzFrffMRMOqRIwhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkprEHRJKlSb6Q5L4k\n25Jc2LWflGRzkge6ryeOuzZJ0j59jCD2AhdV1UrgTOCCJCuBS4AtVbUC2NKtS5J6MvaAqKpdVfXV\nbvl7wHbgFGA1cEPX7QbgnHHXJknap9dzEEmWA68H7gQWVdWubtPjwKJZ3rMuydYkW/fs2TOWOiVp\nIeotIJKcANwC/FZVfXd4W1UV0Lzvvqo2VNV0VU1PTU2NoVJJWph6CYgkxzEIhxur6tNd8xNJFnfb\nFwO7+6hNkjTQx1VMAa4BtlfVVUObNgFru+W1wK3jrk2StE8fs7m+GXgv8LdJ7unaPgRcCWxMcj7w\nMHBuD7VNvCOZLVOSDsXYA6Kq/gaY7bfcWeOsRZI0O++kliQ1GRCSpCYDQpLUZEBIkpoMCElSkwEh\nSWrq4z4ISdrPkdzjU+ubM/PoCDmCkCQ1GRCSpCYDQpLU5DkISUc9z1+MhiMISVKTASFJajIgJElN\nnoPogc90kHQ0cAQhSWoyICRJTR5iOkweJpL0427iRhBJViW5P8mOJJf0XY8kLVQTNYJIcgzwv4Bf\nBHYCX0myqaruG8n+HAVIC16fN9lN+g1+kzaCOAPYUVXfqqofAjcDq3uuSZIWpIkaQQCnAI8Ore8E\n3jTcIck6YF23+v0k94+hrpOB74xhP/PBWkfDWuff0VInzFJr/nt/RyEOsO+D+b6+8mD2MWkBMaeq\n2gBsGOc+k2ytqulx7vNwWetoWOv8O1rqhIVb66QdYnoMWDq0vqRrkySN2aQFxFeAFUlOTfICYA2w\nqeeaJGlBmqhDTFW1N8lvAH8JHANcW1Xbei4LxnxI6whZ62hY6/w7WuqEBVprqpwLXZK0v0k7xCRJ\nmhAGhCSpyYA4SEn+R5JvJLknye1JXtF3TbNJ8pEk3+zq/UySl/VdU0uSdyfZluS5JBN5CeHRMvVL\nkmuT7E5yb9+1zCXJ0iRfSHJf9/d/Yd81zSbJC5PcleTrXa2X913TgSQ5JsnXktw2H59nQBy8j1TV\na6vqdOA24L/1XdABbAZOq6rXAn8HXNpzPbO5F/gPwB19F9IyNPXLLwErgfOSrOy3qlldD6zqu4iD\ntBe4qKpWAmcCF0zw9/UZ4Beq6nXA6cCqJGf2XNOBXAhsn68PMyAOUlV9d2j1J4GJPbtfVbdX1d5u\n9csM7ieZOFW1varGcSf84Tpqpn6pqjuAJ/uu42BU1a6q+mq3/D0Gv9BO6beqthr4frd6XPeayJ/9\nJEuAXwE+Pl+faUAcgiRXJHkU+I9M9ghi2PuBz/ZdxFGqNfXLRP4iO1olWQ68Hriz30pm1x22uQfY\nDWyuqkmt9aPAxcBz8/WBBsSQJJ9Pcm/jtRqgqi6rqqXAjcBvTHKtXZ/LGAznb5zkOrUwJTkBuAX4\nrRkj9IlSVc92h5aXAGckOa3vmmZKcjawu6runs/Pnagb5fpWVW8/yK43An8BrB9hOQc0V61J3gec\nDZxVPd7scgjf00nk1C8jkuQ4BuFwY1V9uu96DkZVPZXkCwzO9UzaxQBvBt6V5JeBFwIvTfLJqnrP\nkXyoI4iDlGTF0Opq4Jt91TKXJKsYDDXfVVVP913PUcypX0YgSYBrgO1VdVXf9RxIkqnnrwJM8iIG\nz6qZuJ/9qrq0qpZU1XIG/07/6kjDAQyIQ3Fld2jkG8A7GFwtMKn+EHgJsLm7LPd/911QS5JfS7IT\n+HfAnyf5y75rGtad6H9+6pftwMYJmfplP0luAr4EvDrJziTn913TAbwZeC/wC92/z3u6//lOosXA\nF7qf+68wOAcxL5eQHg2cakOS1OQIQpLUZEBIkpoMCElSkwEhSWoyICRJTQaENI+6mUofTHJSt35i\nt76838qkQ2dASPOoqh4FPgZc2TVdCWyoqod6K0o6TN4HIc2zbhqJu4FrgQ8Ap1fVj/qtSjp0zsUk\nzbOq+lGS3wE+B7zDcNDRykNM0mj8ErALmLiZP6WDZUBI8yzJ6QwmdTsT+O0ki3suSTosBoQ0j7qZ\nSj/G4BkHjwAfAX6v36qkw2NASPPrA8AjVbW5W78a+Nkkb+2xJumweBWTJKnJEYQkqcmAkCQ1GRCS\npCYDQpLUZEBIkpoMCElSkwEhSWr6fy2llDuomLgdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160fac76048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(42)\n",
    "x = np.random.randn(1000)\n",
    "\n",
    "plt.hist(x,bins=20,color=\"g\")\n",
    "plt.title(\"Example\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b6p+e5fbqFODYnUJ8NL+Z3pUBFtZ6s+KFtSl6dWr5V6R/lWLeqh9nzt+t0vSv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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160fada8a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "labels = ['Dos', 'Cats', 'Birds']\n",
    "sizes = [15, 50, 35]\n",
    "\n",
    "plt.pie(sizes, explode=(0, 0, 0.1), labels=labels, autopct='%1.1f%%',startangle=90)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:cv-nd]",
   "language": "python",
   "name": "conda-env-cv-nd-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
